Troubleshooting Common Issues

Understanding Losses in Solar Plants and Types of Solar Plant Losses, why it is important ? Solar power plants are designed to maximize energy production, but various losses can reduce their efficiency and overall energy yield. Understanding these losses is crucial for improving the performance, reliability, and financial viability of solar energy projects Solar plant losses can be categorized into the following types: 1. Irradiance Losses Shading Losses: Obstructions like buildings, trees, or other solar panels can block sunlight, reducing energy output. Soiling Losses: Accumulation of dirt, dust, or bird droppings on panels reduces the amount of sunlight reaching the solar cells. Atmospheric Losses: Variations in atmospheric conditions like clouds or haze can scatter or absorb sunlight, reducing irradiance. 2. Module-Level Losses Mismatch Losses: Differences in the performance of individual solar cells or modules (due to manufacturing variations or shading) lead to energy losses. Temperature Losses: High temperatures reduce the efficiency of photovoltaic (PV) cells, as their performance decreases with heat. Degradation Losses: Over time, solar panels degrade, producing less energy compared to their initial performance. 3. Inverter Losses Conversion Losses: Inverters convert DC power from solar panels to AC power for grid usage. Inefficiencies in this conversion process cause energy losses. Inverter Downtime: Malfunctions or maintenance-related downtime in inverters can lead to energy production losses. 4. Wiring and Electrical Losses Ohmic Losses: Resistance in electrical wiring causes a portion of the energy to dissipate as heat. Connection Losses: Poor-quality or loose electrical connections can lead to energy losses. Transformer Losses: Transformers used to step up or step down voltage introduce inefficiencies. 5. Operational Losses Maintenance Issues: Delayed or inadequate maintenance can lead to prolonged periods of reduced energy production. Monitoring Gaps: Without real-time monitoring, underperforming components may go unnoticed. 6. Environmental and External Factors Weather Variability: Seasonal and daily variations in sunlight availability affect overall energy production. Grid Curtailment: At times, grid operators may restrict the injection of power from solar plants, leading to energy losses. *Why Understanding Solar Plant Losses Is Important* 1. Maximizing Efficiency By identifying and addressing losses, operators can enhance the overall efficiency of the solar plant, ensuring optimal energy production. Improving Financial Returns 2. Reducing losses directly translates to higher energy output, improving revenue generation and return on investment. 3. Long-Term Reliability Regular monitoring and mitigation of losses ensure that solar plants operate reliably over their intended lifespan. 4. Environmental Impact Improved energy yield means more clean energy is produced, reducing dependence on fossil fuels.

Four wearables. One clinical sleep lab. Every single device got deep sleep wrong by more than double. A The Wall Street Journal columnist did what most reviewers never bother to do. She wore an Oura Ring 5, a Fitbit Air, a Whoop MG, and an Apple Watch Series 11 at the same time, then validated all four against an overnight polysomnogram at Stanford. The polysomnogram measured 28 minutes of deep sleep. Every device reported over an hour. This is the part the wellness market does not want to discuss. Heart rate held up well. All four landed within one beat per minute of the lab. Oura hit resting heart rate exactly. Sleep duration and REM detection were reasonable. The devices are genuinely good at what optical sensors and accelerometers can actually measure. Deep sleep is not one of those things. You cannot infer slow-wave sleep from heart rate and movement. It requires reading brain waves. So the algorithms estimate, and the estimate is off by a factor of two against ground truth. The Stanford clinician framed it correctly. A sleep tracker is a bathroom scale. The absolute number is not the point. The trend over time is. Here is the translation problem, and it is the same one we face in preclinical research. A measurement that looks precise is not the same as a measurement that is valid. Four decimal places of confidence on a number the sensor cannot physically access is not data. It is a guess wearing a lab coat. The fix is not abandoning the devices. It is knowing which endpoints they can defend and which they cannot. Use the heart rate. Watch the trends. Ignore the deep-sleep minute count. The wearables are not lying to you. They are answering a question their sensors were never built to answer. We do the same thing in drug development more often than anyone wants to admit. Credit picture: WSJ

Understanding Losses in Solar Plant Planning – Optimize Before You Energize! Solar power is clean, powerful, and sustainable—but not immune to system losses. Here’s a quick guide to the most common loss factors in solar plant design and operation: 1. Soiling Losses Dust and dirt on panels reduce the amount of sunlight reaching cells, lowering efficiency. 2. Shading Losses Nearby objects like trees or buildings can block sunlight, significantly reducing output. 3. Temperature Losses High temperatures can decrease the efficiency of solar modules, even under full sunlight. 4. DC Losses Energy is lost as heat due to resistance in DC cables and connections before reaching the inverter. 5. Inverter Losses During DC to AC conversion, inverters consume some power internally, causing conversion losses. 6. AC Losses Similar to DC losses, energy is lost in AC cables and transformers before grid or load delivery. 7. Degradation Losses Solar panels lose a small percentage of efficiency every year due to material aging. 8. System Downtime Operational halts from maintenance, faults, or outages reduce overall system performance. 9. Grid Curtailment Sometimes, energy production is intentionally reduced due to grid limitations or regulations. Planning for these losses early helps maximize performance and ROI. #SolarEnergy #CleanTech #Renewables #EnergyLosses #PVSystems #SustainableFuture #GreenEnergy #LinkedInEnergy

API performance issues can silently erode user experience, strain resources, and ultimately impact your bottom line. I've grappled with these challenges firsthand. Here are the critical pain points I've encountered, and the solutions that turned things around: 𝗦𝗹𝘂𝗴𝗴𝗶𝘀𝗵 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗧𝗶𝗺𝗲𝘀 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗨𝘀𝗲𝗿𝘀 𝗔𝘄𝗮𝘆 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Users abandoning applications due to frustratingly slow API responses. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Implementing a robust caching strategy. Redis for server-side caching and proper use of HTTP caching headers dramatically reduced response times. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗕𝗿𝗶𝗻𝗴𝗶𝗻𝗴 𝗦𝗲𝗿𝘃𝗲𝗿𝘀 𝘁𝗼 𝗧𝗵𝗲𝗶𝗿 𝗞𝗻𝗲𝗲𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Complex queries causing significant lag and occasionally crashing our servers during peak loads. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Strategic indexing on frequently queried columns Rigorous query optimization using EXPLAIN Tackling the notorious N+1 query problem, especially in ORM usage 𝗕𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱 𝗳𝗿𝗼𝗺 𝗕𝗹𝗼𝗮𝘁𝗲𝗱 𝗣𝗮𝘆𝗹𝗼𝗮𝗱𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Large data transfers eating up bandwidth and slowing down mobile users. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Adopting more efficient serialization methods. While JSON is the go-to, MessagePack significantly reduced payload sizes without sacrificing usability. 𝗔𝗣𝗜 𝗘𝗻𝗱𝗽𝗼𝗶𝗻𝘁𝘀 𝗕𝘂𝗰𝗸𝗹𝗶𝗻𝗴 𝗨𝗻𝗱𝗲𝗿 𝗛𝗲𝗮𝘃𝘆 𝗟𝗼𝗮𝗱𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Critical endpoints becoming unresponsive during traffic spikes. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Implementing asynchronous processing for resource-intensive tasks Designing a more thoughtful pagination and filtering system to manage large datasets efficiently 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗕𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀 𝗙𝗹𝘆𝗶𝗻𝗴 𝗨𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗥𝗮𝗱𝗮𝗿 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Struggling to identify and address performance issues before they impact users. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Establishing a comprehensive monitoring and profiling system to catch and diagnose issues early. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗮𝘀 𝗨𝘀𝗲𝗿 𝗕𝗮𝘀𝗲 𝗚𝗿𝗼𝘄𝘀 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: What worked for thousands of users started to crumble with millions. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Implementing effective load balancing Optimizing network performance with techniques like content compression Upgrading to HTTP/2 for improved multiplexing and reduced latency By addressing these pain points head-on, we can significantly improve user satisfaction and reduce operational costs. What challenges have you faced with API performance? How did you overcome them? Gif Credit - Nelson Djalo

Every PM wants to measure the success of their product. But most struggle to do it correctly. As a product management hiring manager, leader, and coach, I've seen that many product managers struggle with defining the right success metrics They focus on generic metrics like acquisition, engagement,  retention These are insufficient. My recommendation is to ask concrete questions when thinking of metrics Here's a list of questions I ask: 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗳𝗶𝗿𝘀𝘁 1. What is the user’s goal? 2. What human need do they want to fulfill? 3. What action signifies that their need is met? 4. Is that action enough to know user’s job is done? 5. How can I measure that action? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘂𝘀𝗮𝗴𝗲 𝗮𝗻𝗱 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 1. How many users are using the product? 2. How many users should be using it? 3. Which users aren't using it but should be using it? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗵𝗼𝘄 𝗺𝘂𝗰𝗵 𝘂𝘀𝗲𝗿𝘀 𝗲𝗻𝗷𝗼𝘆 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 1. How many users like the product? 2. How much do they like it? 3. What action(s) show they “like” it? 4. How can I measure those actions 5. Do they like it enough to keep coming back? 6. If yes, how often should they come back? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘄𝗵𝗶𝗹𝗲 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 1. Are users finding it hard to complete certain actions? 2. Are there things that users dislike? 3. Are there enough options for users to choose from? 4. Are there things that users want to do, but the product doesn’t allow them to? 5. Can we measure all the above? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 1. Can I cheat on any of the above metrics? 2. Do above metrics give the most accurate answer? 3. Are all metrics simple enough for everyone to understand? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗻𝗲𝘁 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝘁𝗵𝗲 𝗼𝘃𝗲𝗿𝗮𝗹𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁/𝗰𝗼𝗺𝗽𝗮𝗻𝘆 1. Are  above metrics a true representation of success? 2. Any other parts of user journey I should measure? 3. Will a positive impact on above metrics lead to a negative impact on other critical metrics? 4. Is the tradeoff acceptable? -- How easy or tough do you find creating success metrics? What is your process?

The secret to embracing technical quality in product management that unlocks long-term success: Stop treating it like an afterthought. "But isn’t shipping features more important than backend fixes?" - you probably ask. No. Here’s why: • A slow, clunky product with random failures? Users leave. Fast. • Neglecting stability means downtime, crashes, and bugs that silently erode user trust. • Fixing performance later is costly, time-consuming, and painful. Just ask any PM who ignored it I learned this the hard way. Early in my career, I focused on delivering new features. I dismissed “stability work” as something to handle later. And at first? It seemed fine. 👍 Users were happy. 😀 Growth was steady. 📈 Until it wasn’t. 😱 A small bug in one update crippled a key feature. A long-awaited success turned into one nightmare of a night. Random crashes turned into support nightmares. Costly contract discounts had to be offered to keep clients. Database slowness forced us to pause everything and rebuild our databases with new indexes. It's clear as day: Performance is a product feature. It’s just as crucial as UX or functionality. Thus, a question naturally arises: "How do you know if you’re handling stability the right way?" Ask yourself: ✔ Do you listen to your team's advice? ✔ Are you regularly fixing high-impact bugs? ✔ Do you have automated crash reporting & alerts? ✔ Do you build with stability and scalability in mind? ✔ Do you book time to find an optimal tech solution? ✔ Have you set up a process to decide which bugs to fix? ✔ Do you find time for code refactors that are truly needed? ✔ Are security vulnerabilities proactively tested and patched? ✔ Is your database optimized with proper indexing and query tuning? ✔ Is performance a recurring discussion, not an emergency reaction? ✔ Do you monitor downtime trends and investigate even small outages? ✔ Do you test under real-world conditions, not just in a perfect dev setup? ✔ Do you stress-test your system under peak load conditions before it’s too late? ✔ Do you set a threshold where bug fixes take priority over new feature development? ✔ Do you optimize performance for low-end devices and slow networks, not just the latest hardware? Stability, scalability, and performance aren’t just technical concerns. Those are the fundamentals that allow your product to operate in the first place. Ignore it at your own risk. Do you believe you dedicate enough time to keep the product 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭𝘭𝘺 healthy? Looking forward to reading your comments. #productmanagement #productmanager #technicalexcellence

I tracked sleep for 30 days. Almost tanked my sleep in the process. There’s a term for it now. Orthosomnia: the obsession with perfect sleep scores that ironically ruins your sleep. Three brutal truths: 1. Data ≠ Biology. Trackers get time asleep mostly right. But REM, deep sleep, latency? They’re guessing. Yet we chase those numbers as if they were gospel. 2. Stress transfers. I found myself lying awake, anxious because my tracker said I’d slept badly. Self-fulfilling insomnia. 3. We’re human, not robots. Normal sleep fluctuates. 3–6 nightly wake-ups? Normal. But one “poor” score and your brain hits panic mode. So I ran the experiment in reverse. Ditched the Oura. Went pen-and-paper. Logged one thing: how I felt at 7 a.m. Result? Better sleep. Less rumination. And a painful realization: Sleep isn’t a performance metric. It’s biology. The relentless pursuit of 8 hours, 25% deep, no wake-ups? It’s a fantasy. Precision kills. It introduces anxiety where calm is needed. Track if it helps. But if your sleep stack is stressing you out? The most powerful optimization might be letting go. #Recover #UpwardARC

Everyone is monitoring their sleep these days, right? But what might be the problems and pitfalls with this? 🛌 While sleep trackers offer precise categorisation of sleep and wake, they (currently) can fall short in detecting different sleep stages as research comparing data to gold standard, lab-based polysomnography (PSG) has demonstrated: https://lnkd.in/eXyWdn9Q 😪 The social phenomenon "ORTHOSOMNIA" has been described as the obsessive pursuit of optimal sleep metrics. The constant pursuit of an "optimal" sleep duration can create undue stress and anxiety, ultimately counteracting performance gains. 👀 Athletes and practitioners alike should be aware of which metrics they can (and cannot) rely on - simplifying complex constructs like recovery and readiness into one number is appealing yet scientifically flawed. 🛫 The very nature of sport with its unrelating schedule, travel and high levels of stress is seldom conducive to optimal sleep. Athletes (and practitioners) often find themselves battling unfamiliar sleeping environments, making it a challenging task to achieve perfect sleep routines. 📊 Increasing personal wearable devices means data privacy and security have come to the forefront. Measures must be in place to ensure athletes' data rights are protected, maintaining trust and ethical practices. But let's be clear: I advocate for sleep tracking! However, it's worth being mindful of potential drawbacks and approaching this data with a balanced perspective. As with many things in (sports) science, context and individual variations play a vital role. Interested to read more? Check out the full post on the Global Performance Insights blog to read my 8 key strategies to optimise sleep tracking in sports science 👇 🔗 https://lnkd.in/e_CizEE4 #Sleep #Technology #SportsScience

5 research questions that uncover what users won’t say out loud. Polite answers won’t build great products. These questions are the ones that force people to think and lead to useful product insights: 👉 “Tell me about the last time you tried to do [task] and gave up.” People don’t bring up failure unless you ask. But when they do, they show you where your product actually breaks 👉 “Is there anything in [product] without which you won't use it?” Reveals true dependencies vs. nice-to-haves. Users will tell you the one thing holding their workflow together. It’s rarely what you expect. 👉 “Walk me through what you were thinking during that 30-second pause.” Ask this in the moment, not after. It surfaces hesitation, mental model gaps, and quiet confusion that observation alone won’t catch. 👉 “What’s something about this that didn’t behave as you expected?” This is great for spotting subtle friction that users may not verbalize on their own 👉 “What’s the workaround you’ve created for this?” People invent hacks to survive broken flows & that can be gold.  Workarounds show what users need but can’t articulate. What’s the one question you always ask no matter what?