Fault Detection and Diagnosis in Grids

Your Distance Relay Is Lying to You About Fault Location This is something that is kind of interesting because fault location is a real issue for utilities as their systems span large areas and sometimes over rough terrain. It is really important to send crews as close as possible to where we think the fault was based on the fault voltages and currents so they are not wasting time driving around trying to locate the problem. The first idea that comes to most people’s heads is to use distance or impedance relaying measurements to locate the fault, after all distance is in its name. But this would only be a half truth, as the impedance seen by the distance relay would include the fault’s impedance, which you will never know. It will usually get you close, as fault impedance usually is smaller than line impedance, but that is not always the case. Transmission lines have fractions of an ohm of resistance and reactance per span, so it is very easy to send crews to inspect the wrong section of a transmission line. In addition to fault impedance, load current itself will change the impedance calculation to the fault, as it will affect the voltages and currents that you are using to make your distance measurement. One way to get around this is to use the Takagi method for fault location. This method gets around the issue with fault impedance by recognizing that the fault impedance is going to be purely resistive and the transmission lines themselves are very reactive. The reactive power flowing into the line during the fault is going to be consumed by the line segment reactance. This means that the reactive power = imaginary(VI*) = n·X_L·I_fault², with n being the percentage of the line length and X_L being the line reactive impedance. I_fault is going to be largely reactive just due to how much reactance is in the grid from generation to the fault. The issue earlier with load current affecting this measurement is washed out by using superposition to remove the voltage and current changes from the measured fault voltages and currents. Instead of using the raw fault voltages and currents talked about above, you are using the voltages and currents that would have been there without load power flow. Superposition allows you to remove this influence on the result. This method allows you to approximate a fault’s location while limiting the impact of fault impedance. It works better at transmission than distribution, as distribution tends to be more resistive. This makes the approximation of eliminating the line resistance and fault resistance a more nebulous. There are other methods to approximating fault location. Probably the most accurate is the traveling wave method, which has been used since the 70s and works by timing how long an impulse takes to reach each end of a line from a fault. #utilities #electricalengineering #renewables #energystorage #substations #grid

☀ Distance Protection Challenges in Presence of Grid-Forming Inverters In the paper "Impacts of Grid‐Forming Inverters on Distance Protection" by D. Johansson et al., published in IET Generation, Transmission & Distribution (2025), the authors investigate how the integration of Grid-Forming Inverters (GFMs) introduces new challenges for traditional protection schemes. One significant issue arises in distance protection, particularly due to the fundamentally different fault behavior of GFMs compared to synchronous machines. 🧠 Key Insight from Recent Research: The study reveals that distance relays, designed with the expectation of strong fault current contributions from synchronous generators, may malfunction or underreach when operating in inverter-dominated grids. 📉 What Happens During a Fault? GFMs are designed to limit their fault current output to protect inverter hardware. As a result: The apparent impedance seen by distance relays increases, often exceeding the protection zone. This leads to delayed or failed relay tripping, particularly in areas with high GFM penetration. 📊 Simulation-Based Evidence (Figures 12–15): To visualize these effects, the authors simulate a single-line-to-ground fault scenario. Here's what the key figures demonstrate: Figure 12 – Apparent Impedance at R1: The relay’s measured impedance stays outside Zone 1, despite the fault location being within it. This is a classic underreaching issue caused by current-limited GFM behavior. Figure 13 – Positive Sequence Voltage at Bus 3: The voltage remains relatively stable due to the GFM's control loop, reducing the voltage dip typically used as a fault indicator in traditional schemes. Figure 14 – Positive Sequence Current at R1: Fault current magnitude is significantly lower, causing the impedance calculation to overestimate distance and miss the fault. Figure 15 – GFM Current Injection: The inverter’s fault current saturates quickly, showing a flat and controlled response, protecting the device but undermining impedance-based logic. ⚙️ Implications for Power System Protection: This analysis suggests a strong need to: Develop adaptive or communication-based protection schemes. Reassess relay zone settings and coordination. Investigate hybrid approaches that incorporate non-impedance-based fault detection. Revising protection strategies becomes critical to ensure system security and reliability as we transition toward inverter-dominated, low-inertia grids. 📖 Reference: Johansson, D. et al. “Impacts of Grid‐Forming Inverters on Distance Protection”, IET Generation, Transmission & Distribution, 2025. #PowerProtection #GridForming #InverterControl #DistanceRelay #PowerSystemSecurity #GFM #ProtectionEngineering #RenewableGrid #FutureGrid #RelayCoordination

I didn’t truly understand protection challenges until I started working with systems that had a high share of inverter-based resources. On paper, protection looks simple: fault happens -> current spikes -> relay trips. In reality, that logic was built for synchronous machines. With rotating generators, faults are loud. Current shoots up 5–8 times rated. Voltage collapses clearly. Phase angles swing in a predictable, physics-driven way. Relays see the fault instantly. Now compare that with IBR-dominated systems. Fault current barely reaches 1.1–1.3 pu. Waveforms are shaped by control algorithms. Current limiting, PLL dynamics, and ride-through logic all kick in. What looks like a fault to the network can look like a “normal operating point” to a conventional relay. That’s where protection blinding becomes very real — not a theoretical risk. This is not about IBRs being “bad”. It’s about the fact that we are using protection philosophies designed for a different era. Modern grids dont fail because protection is wrong. They fail because protection assumptions are outdated. The future of protection is not: ❌ higher current thresholds ❌ more aggressive settings It’s: ✅ waveform intelligence ✅ faster measurements ✅ grid-forming behaviour ✅ protection designed with controls, not against them The grid has changed. Protection has to catch up. #PowerSystems #GridProtection #EnergyTransition #InverterBasedResources #PowerEngineering #GridModernization #ElectricalEngineering #FutureGrid #EnergySystems

Inaccurate inverter-based resource (IBR) models are widely recognized as a key contributor to instability and outage events in recent years. One major issue: models are often incorrectly parameterized. Default or placeholder values, undocumented control updates, and the lack of validation against field data all lead to inaccurate models that fail to capture true IBR behaviors, which can mislead grid planning and operations. 👉 Our latest research offers a solution. In a recent paper accepted to IEEE Transactions on Power Systems, we present a parameter error identification method that efficiently validates and calibrates IBR models using grid disturbance data. Instead of blindly estimating all parameters — which is often intractable — our approach can provably pinpoint which parameter(s) are wrong and effectively correct them. ✨ Key strengths of our method: - Exactly identifies and corrects the specific culprit parameters causing model–measurement discrepancies, among a large set of parameters in IBR models that are formidable to tune. - Provably distinguishes whether a discrepancy comes from model error (wrong parameter) or measurement error (corrupted sensor data). This is crucial for the method's practicality, since field data are rarely “clean.” Learn more here: https://lnkd.in/eu3F-yc8 #InverterBasedResources #Stability #Ocsillation #ModelValidation #ParameterEstimation #RenewableEnergy #GridInterconnection #PowerSystems

🆕 If you are working on microgrids, distribution protection, or grid integration of renewables and EVs, I hope this post becomes a useful reference. New publication in Renewable and Sustainable Energy Reviews (Impact Factor: 16.3) 🎉 🔓 Open Access Title: Protection strategies for ADNs: A comprehensive review Link: https://lnkd.in/dneCZz6V As active distribution networks (ADNs) rapidly fill with DERs, ESSs, and EVs, legacy protection schemes are being pushed beyond their comfort zone. Our new review maps the state of the art and lays out a practical roadmap for resilient protection in both AC and DC microgrids. Insights: • Improved protection strategies tailored to DERs, ESSs, and EV-rich ADNs • How to adapt conventional protection to emerging microgrid challenges • Why DC microgrid protection needs attention, especially circuit breaker limitations • Advanced fault detection approaches to boost AC/DC microgrid reliability • The case for adaptive protection coordination in networks with high RES and EV penetration Special thanks to Mohammad Mahdi Abedi, Prof. Hamid Reza Baghaee, Prof. Mahmoud Reza Haghifam, and Prof. Pierluigi Siano #ADN #Microgrids #DER #EV #EnergyStorage #Protection #PowerSystems #Renewables #GridModernization #OpenAccess #NewPublication

Wonder why your #IBR-based plant had a strange response and you can't figure it out from the terminal measurement data? Stop guessing if your #IBR plant's control mode. We've developed a statistical method to infer it from ambient #PMU / #synchrophasor data, helping diagnose grid stability issues related to #IBRs Chetan Mishra of Dominion Energy led the work in this paper which will be presented at the #HICCS 2026 Electrical Energy Systems track. Here are some highlights on our paper: https://lnkd.in/e2RgiPmx 😎 A novel approach to deduce the reactive power control mode of #IBR plants using only ambient synchrophasor data. 🤓 The method uses system identification techniques and statistical tests, eliminating the need for accurate plant models that are often not properly validated, unreliable or even unavailable to utilities. 👷♂️ We addresses the real-world challenge of "control mode switching," which can happen automatically or manually without the knowledge of system operators, making it difficult to interpret measured dynamics and diagnose stability problems. ⚡ The paper's method was successfully tested on both synthetic data and real-world data from a PV plant in Dominion Energy's power system. The analysis revealed that the plant was not operating in its expected voltage control mode, providing valuable diagnostic insights. This work offers a step forward in our efforts for the development of methods for continuous monitoring and diagnostics of renewable energy resources on the power grid that can be applied in the real-world today! Thanks to Dominion Energy for allowing me to continue work with the Engineering Analytics & Modeling team: Chetan Mishra, Bikal Pudasaini, Jaime De La Ree and Kevin Jones, doing research with purpose and real-world application.

Solar Performance Monitoring: Practical Examples with Fault Analysis To understand how data analysis helps in fault detection and performance optimization, let’s look at real-world scenarios with sample values. Example 1: Underperformance Due to Soiling Losses 🔹 Expected Power Output: 500 kW 🔹 Actual Power Output: 450 kW 🔹 Performance Ratio (PR) = (450 / 500) × 100 = 90% ✅ (Good) After a week: 🔹 Expected Power Output: 500 kW 🔹 Actual Power Output: 400 kW 🔹 PR = (400 / 500) × 100 = 80% ⚠ (Declining) 🔹 Soiling Loss Estimate: 10-12% 📌 Diagnosis: Increased dust accumulation on panels is reducing efficiency. 📌 Action: Schedule panel cleaning and monitor PR improvement. Example 2: Inverter Failure Leading to Downtime 🔹 Total Plant Capacity: 1 MW 🔹 Number of Inverters: 10 (Each handling 100 kW) 🔹 Before Issue: • Expected Output: 950 kW (considering minor losses) • Actual Output: 940 kW ✅ (Good Performance) 🔹 After Issue: • Expected Output: 950 kW • Actual Output: 840 kW ⚠ (Significant Drop) • Inverter Logs: • Inverter 6: No output • Fault Code: Overvoltage error 📌 Diagnosis: One inverter failure resulted in a 100 kW generation loss. 📌 Action: Restart the inverter remotely via SCADA, if unsuccessful, perform on-site inspection for hardware issues. Example 3: Faulty Solar Panel String Detection 🔹 Total Plant Capacity: 500 kW 🔹 Number of Strings: 50 (Each handling 10 kW) 🔹 Normal Operation: • Each string generating 9.5 - 10 kW 🔹 Current Readings: • 49 Strings: 9.8 kW ✅ (Normal) • 1 String: 6.5 kW ⚠ (Underperforming) 📌 Diagnosis: Possible issues include: ✅ Loose connection in the junction box. ✅ Module degradation in one or more panels. ✅ Partial shading from nearby object. 📌 Action: Perform IR thermographic scanning to check for hotspots and replace faulty panels if needed. Example 4: Impact of High Temperature on Efficiency 🔹 Ambient Temperature: 45°C 🔹 Panel Temperature: 70°C 🔹 Power Output Drop: 5-6% compared to normal conditions 📌 Diagnosis: High temperatures reduce panel efficiency due to the negative temperature coefficient (-0.5% per °C above 25°C). 📌 Action: ✅ Install cooling solutions (e.g., water mist or ventilation). ✅ Use bifacial or high-temperature-resistant panels for future installations. Example 5: Grid Instability Causing Shutdown 🔹 Normal Grid Voltage: 415V 🔹 Recorded Grid Voltage: 470V ⚠ (Overvoltage) 🔹 Inverter Logs: “Grid Overvoltage Protection Activated – Shutdown Initiated” 📌 Diagnosis: ✅ Overvoltage from the grid triggered the inverter’s protective shutdown. ✅ Possible transformer tap setting issue or reactive power injection problem. 📌 Action: ✅ Coordinate with the grid operator to stabilize voltage fluctuations. ✅ Enable reactive power control in the inverter to manage voltage spikes. #SolarMonitoring #DataAnalytics #IoT #SCADA #PredictiveMaintenance #RenewableEnergy #IliosPower

⚡ Fault Masking in Renewable Plants — A Hidden Challenge in Grid Compliance In many wind and solar plants, engineers often assume that the voltage dip at the Point of Interconnection (POI) will be directly reflected at the inverter or WTG terminals. However, in practice this is not always true. Consider a scenario where a shallow grid fault causes the POI voltage to drop below 0.85 pu. According to grid codes, the plant should respond with Low Voltage Ride Through (LVRT) behavior and inject reactive current to support the grid. But inside the plant, the inverter terminals may still see 0.90–0.92 pu voltage due to: • Collector system impedance • Step-up transformers • Long MV cables • Reactive power devices such as STATCOM/SVG Because the LVRT logic in many inverters is triggered by local terminal voltage, the turbine may not recognize that a grid fault has occurred. The result? • The Plant Power Controller (PPC) may freeze active power. • But WTGs/inverters may not enter LVRT mode. • Reactive current injection required by the grid code may not be delivered. This phenomenon is commonly referred to as “Fault Masking by the Collector Network.” It becomes more pronounced under low generation conditions. When plant current is low, the voltage drop across collector cables and transformers is small, meaning the internal plant voltage remains relatively healthy even when the POI voltage dips. So how do plant designers handle this? Modern renewable plants implement several solutions: 1️⃣ POI-based fault detection in the PPC The PPC monitors POI voltage and sends a fault flag to all turbines/inverters, forcing them into fault response mode even if their terminal voltage appears normal. 2️⃣ Remote voltage supervision Some OEMs feed POI voltage measurements directly to turbine controllers, ensuring LVRT decisions consider the weakest voltage in the system. 3️⃣ Reactive current dispatch from PPC Instead of relying purely on local inverter logic, the PPC may issue reactive current commands during grid faults, but this often results in delayed response & may not fulfill the requirement. 4️⃣ Conservative LVRT thresholds Some turbines trigger LVRT at slightly higher voltage levels to ensure grid support begins earlier. As renewable penetration increases, these internal plant dynamics are becoming increasingly important. Grid operators are no longer only concerned with turbine-level performance but with the overall plant response at the POI. Understanding and mitigating fault masking is therefore critical to ensuring reliable grid support from inverter-based resources. Looking forward for insights from experts!

Key Power System Protection Challenges in BESS + Solar/Wind Systems: 1. ⚡ Low Fault Current Contribution Inverter-based resources (IBRs) limit fault current to ~1.1–1.5 × rated current. Traditional overcurrent protection (50/51) becomes ineffective. Challenge for: Feeder protection Busbar protection Backup protection coordination 🛠️ Mitigation: Use differential protection (87) or distance protection (21). Employ IEC 61850 GOOSE logic for fast tripping schemes. 2. 🔁 Bidirectional Power Flow Protection must detect and respond to faults regardless of power direction. Impacts: Transformer protection (needs directional sensing) Reverse power protection (32R) Islanding detection schemes 🛠️ Mitigation: Use directional overcurrent (67/67N). Integrate with PPC/EMS logic for islanding and grid-following modes. 3. 🧲 Islanding and Anti-Islanding Protection Inverters may continue energizing a disconnected grid (unintentional islanding). Passive schemes (voltage, frequency) may not detect it promptly. 🛠️ Mitigation: Use active anti-islanding techniques (e.g., Sandia method, impedance shifting). Integrate ROCOF (df/dt) and vector shift relays (per G99 or grid code). 4. 🔌 Coordination Between Grid Code & Protection Grid codes like G99 (UK), NERC PRC (US), or CEA (India) require: Voltage/frequency ride-through LVRT/HVRT logic Fault ride-through (FRT) behavior Inverter protection must coordinate with utility protection. 🛠️ Mitigation: Implement adaptive protection logic in relays or PPC. Use grid-compliant protection relays (e.g., SMA615, P345, SIPROTEC 7SJ85). 5. 🧯 Transformer Differential Protection with Inverters Inverter inrush and asymmetry may cause false tripping in 87T. Also, transformer energization from BESS may appear as internal fault. 🛠️ Mitigation: Use harmonic restraint and inrush blocking in the 87T relay. Tune sensitivity thresholds based on commissioning data. 6. 🧠 Fast Dynamics of BESS Protection needs to operate faster than traditional systems. Energy management and protection must interact coherently. 🛠️ Mitigation: Use high-speed differential relays (e.g., RED615, SEL-487E). Implement EMS-PPC-protection integration via IEC 61850/GOOSE. 7. 🌩️ Grid Weakness and Stability BESS and solar may operate in weak grids with high impedance. Fault detection becomes less reliable due to lower fault current and poor signal quality. 🛠️ Mitigation: Use distance relays with quadrilateral characteristics. Apply positive-sequence overvoltage or ROCOF relays. 8. 🧮 Protection Settings Complexity Multiple modes: Grid-following, islanded, black start, etc. Requires mode-dependent settings. 🛠️ Mitigation: Use group setting selection (GSS) in relays. Automate settings switching through EMS/PMS.

$2.5M in outage costs 10,000+ customer complaints A black eye with regulators that will take years to heal This is what a single undetected line fault spiraled into Here’s why we say “routine inspections” are not enough. The degradation had been there for weeks hiding in plain sight. The crews never caught it. Why? Traditional ground and tower inspections are reactive, labor-intensive, and blind to early warning signs. Every week of undetected line degradation quietly racks up millions in risk exposure. But here’s what can change: 🔍 Advanced aerial inspections with sensor fusion. RGB Zoom imaging found loose cotter pin Thermal imaging revealed hotspots invisible to the eye LiDAR highlighted subtle line sag and vegetation encroachment The result: Faults spotted 3–5 weeks earlier Crews deployed only where needed (safer + faster) Millions saved in avoided downtime and regulatory penalties Looking at your infrastructure is not the same as seeing it. “Routine checks” catch today’s problems. Sensor fusion inspections prevent tomorrow’s disasters. If you’re running utility operations and want to know whether your current inspection program is catching faults early enough, let's talk. #Utilities #Dronetechnology #Sensorfusion #Infrastructure #Gridreliability